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Journal articles on the topic 'Quantum circuit learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Lukac, Martin, and Marek Perkowski. "Inductive learning of quantum behaviors." Facta universitatis - series: Electronics and Energetics 20, no. 3 (2007): 561–86. http://dx.doi.org/10.2298/fuee0703561l.

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In this paper studied are new concepts of robotic behaviors - deterministic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not given as examples) are then converted not only to deterministic cares as in the classical learning, but also to output values generated wit

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Menegasso Pires, Otto, Eduardo Inacio Duzzioni, Jerusa Marchi, and Rafael De Santiago. "Quantum Circuit Synthesis Using Projective Simulation." Inteligencia Artificial 24, no. 67 (2021): 90–101. http://dx.doi.org/10.4114/intartif.vol24iss67pp90-101.

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Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use.These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored.We studied the viability of using Projective Simulation,

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Ferrari, Davide, and Michele Amoretti. "Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices." International Journal of Quantum Information 16, no. 08 (2018): 1840006. http://dx.doi.org/10.1142/s0219749918400063.

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Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger–Horne–Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform qua

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Chen, Chih-Chieh, Masaya Watabe, Kodai Shiba, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. "On the Expressibility and Overfitting of Quantum Circuit Learning." ACM Transactions on Quantum Computing 2, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3466797.

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Applying quantum processors to model a high-dimensional function approximator is a typical method in quantum machine learning with potential advantage. It is conjectured that the unitarity of quantum circuits provides possible regularization to avoid overfitting. However, it is not clear how the regularization interplays with the expressibility under the limitation of current Noisy-Intermediate Scale Quantum devices. In this article, we perform simulations and theoretical analysis of the quantum circuit learning problem with hardware-efficient ansatz. Thorough numerical simulations show that t

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Zhu, D., N. M. Linke, M. Benedetti, et al. "Training of quantum circuits on a hybrid quantum computer." Science Advances 5, no. 10 (2019): eaaw9918. http://dx.doi.org/10.1126/sciadv.aaw9918.

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Generative modeling is a flavor of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset using a quantum-classical hybrid machine. The training proceeds by running parameterized circuits on a trapped ion quantum computer and feeding the results to a classical optimizer. We apply two separate strategies, Particle Swar

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Hu, Ling, Shu-Hao Wu, Weizhou Cai, et al. "Quantum generative adversarial learning in a superconducting quantum circuit." Science Advances 5, no. 1 (2019): eaav2761. http://dx.doi.org/10.1126/sciadv.aav2761.

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Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rou

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Griol-Barres, Israel, Sergio Milla, Antonio Cebrián, Yashar Mansoori, and José Millet. "Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals." Applied Sciences 11, no. 14 (2021): 6427. http://dx.doi.org/10.3390/app11146427.

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Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problem

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8

Stokes, James, and John Terilla. "Probabilistic Modeling with Matrix Product States." Entropy 21, no. 12 (2019): 1236. http://dx.doi.org/10.3390/e21121236.

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Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models. The gradient-free algorithm, presented as a sequence of exactly solvable effective models, is a modification of the density matrix renormalization group procedure adapted for learning a probability distribution. The conclusion that circuit-based models offer a useful inductive bias for classical datasets is supported by experimental results on the par

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Ren, Wanghao, Zhiming Li, Yiming Huang, et al. "Quantum generative adversarial networks for learning and loading quantum image in noisy environment." Modern Physics Letters B 35, no. 21 (2021): 2150360. http://dx.doi.org/10.1142/s0217984921503607.

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Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend q

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Benedetti, Marcello, Edward Grant, Leonard Wossnig, and Simone Severini. "Adversarial quantum circuit learning for pure state approximation." New Journal of Physics 21, no. 4 (2019): 043023. http://dx.doi.org/10.1088/1367-2630/ab14b5.

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11

Watabe, Masaya, Kodai Shiba, Chih-Chieh Chen, Masaru Sogabe, Katsuyoshi Sakamoto, and Tomah Sogabe. "Quantum Circuit Learning with Error Backpropagation Algorithm and Experimental Implementation." Quantum Reports 3, no. 2 (2021): 333–49. http://dx.doi.org/10.3390/quantum3020021.

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Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high-dimensional state or function approximation. Quantum–classical hybrid algorithms have been proposed in recent years for this purpose under the noisy intermediate-scale quantum computer (NISQ) environment. Under this scheme, the role played by the classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circu

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Mills, Daniel, Seyon Sivarajah, Travis L. Scholten, and Ross Duncan. "Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack." Quantum 5 (March 22, 2021): 415. http://dx.doi.org/10.22331/q-2021-03-22-415.

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Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational quantum eigensolver algorithm), shallow (inspired by IQP-type circuits that might be useful for near-term quantum machine learning), and square (inspired by the quantum volume benchmark). We quantify the performance of a quantum computing system in running circuits from these classes using several figures of merit, all of which require exponential classical

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13

Li, Yiwei, Edison Tsai, Marek Perkowski, and Xiaoyu Song. "Grover-based Ashenhurst-Curtis decomposition using quantum language quipper." Quantum Information and Computation 19, no. 1&2 (2019): 35–66. http://dx.doi.org/10.26421/qic19.1-2-4.

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Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach based on Grover’s search algorithm for a generalized Ashenhurst-Curtis decomposition. The method models the decomposition problem as a search problem and constructs the oracle circuit based on the set-theoretic partition algebra. A hybrid quantum-based algorithm takes advantage of the quadratic speedup achieved by Grover’s search algorithm with quantum oracles for finding the minimum-cost decomposition

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Killoran, Nathan, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. "Strawberry Fields: A Software Platform for Photonic Quantum Computing." Quantum 3 (March 11, 2019): 129. http://dx.doi.org/10.22331/q-2019-03-11-129.

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We introduce Strawberry Fields, an open-source quantum programming architecture for light-based quantum computers, and detail its key features. Built in Python, Strawberry Fields is a full-stack library for design, simulation, optimization, and quantum machine learning of continuous-variable circuits. The platform consists of three main components: (i) an API for quantum programming based on an easy-to-use language named Blackbird; (ii) a suite of three virtual quantum computer backends, built in NumPy and TensorFlow, each targeting specialized uses; and (iii) an engine which can compile Black

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Mari, Andrea, Thomas R. Bromley, Josh Izaac, Maria Schuld, and Nathan Killoran. "Transfer learning in hybrid classical-quantum neural networks." Quantum 4 (October 9, 2020): 340. http://dx.doi.org/10.22331/q-2020-10-09-340.

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We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-th

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Khairy, Sami, Ruslan Shaydulin, Lukasz Cincio, Yuri Alexeev, and Prasanna Balaprakash. "Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2367–75. http://dx.doi.org/10.1609/aaai.v34i03.5616.

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Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational hybrid quantum-classical algorithm for approximately solving combinatorial optimization problems. The quality of the solution obtained by QAOA for a given problem instance depends on the performance of the classical optimizer used to optimize the variational parameters. In this p

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Belis, Vasilis, Samuel González-Castillo, Christina Reissel, et al. "Higgs analysis with quantum classifiers." EPJ Web of Conferences 251 (2021): 03070. http://dx.doi.org/10.1051/epjconf/202125103070.

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We have developed two quantum classifier models for the ttH classification problem, both of which fall into the category of hybrid quantumclassical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance, in specific cases of low number of training samples, with respect to conventional ML methods even with a limited number of qubits available in current hardware. To utilise algorithms with a low number of qubits — to accommodate for limit

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18

Jiang, Zhang, Amir Kalev, Wojciech Mruczkiewicz, and Hartmut Neven. "Optimal fermion-to-qubit mapping via ternary trees with applications to reduced quantum states learning." Quantum 4 (June 4, 2020): 276. http://dx.doi.org/10.22331/q-2020-06-04-276.

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We introduce a fermion-to-qubit mapping defined on ternary trees, where any single Majorana operator on an n-mode fermionic system is mapped to a multi-qubit Pauli operator acting nontrivially on ⌈log3⁡(2n+1)⌉ qubits. The mapping has a simple structure and is optimal in the sense that it is impossible to construct Pauli operators in any fermion-to-qubit mapping acting nontrivially on less than log3⁡(2n) qubits on average. We apply it to the problem of learning k-fermion reduced density matrix (RDM), a problem relevant in various quantum simulation applications. We show that one can determine i

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19

Gonzalez-Raya, Tasio, Enrique Solano, and Mikel Sanz. "Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials." Quantum 4 (January 20, 2020): 224. http://dx.doi.org/10.22331/q-2020-01-20-224.

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The Hodgkin-Huxley model describes the conduction of the nervous impulse through the axon, whose membrane's electric response can be described employing multiple connected electric circuits containing capacitors, voltage sources, and conductances. These conductances depend on previous depolarizing membrane voltages, which can be identified with a memory resistive element called memristor. Inspired by the recent quantization of the memristor, a simplified Hodgkin-Huxley model including a single ion channel has been studied in the quantum regime. Here, we study the quantization of the complete H

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Gokhale, Angelina, Mandaar B. Pande, and Dhanya Pramod. "Implementation of a quantum transfer learning approach to image splicing detection." International Journal of Quantum Information 18, no. 05 (2020): 2050024. http://dx.doi.org/10.1142/s0219749920500240.

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In this paper, we present an implementation of quantum transfer learning to blind and passive detection of image splicing forgeries. Though deep learning models are becoming increasingly popular for various computer vision use cases, they depend on powerful classical machines and GPUs for dealing with complex problem solving and also to reduce the time taken for computation. The quantum computing research community has demonstrated elegant solutions to complex use cases in deep learning and computer vision for reducing storage space and increasing the accuracy of results compared to those obta

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Meinhardt, Nicholas, Bastiaan Dekker, Niels M. P. Neumann, and Frank Phillipson. "Implementation of a Variational Quantum Circuit for Machine Learning with Compact Data Representation." Digitale Welt 4, no. 1 (2019): 95–101. http://dx.doi.org/10.1007/s42354-019-0242-3.

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22

Lin, Hui, Dongsheng Liu, Cong Zhang, and Yahui Dong. "Design and Implementation of a Lattice-Based Public-Key Encryption Scheme." Journal of Circuits, Systems and Computers 27, no. 13 (2018): 1850201. http://dx.doi.org/10.1142/s0218126618502018.

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Due to its advantage of quantum resistance and the provable security under some worst-case hardness assumptions, lattice-based cryptography is being increasingly researched. This paper tries to explore and present a novel lattice-based public key cryptography and its implementation of circuits. In this paper, the LWE (learning with error) cryptography is designed for circuit realization in a practical way. A strategy is proposed to dramatically reduce the stored public key size from [Formula: see text] to [Formula: see text], with only several additional linear feedback shift registers. The ci

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23

Zheng, Yi, Haobin Shi, Wei Pan, Quantao Wang, and Jiahui Mao. "Quantum Hacking on an Integrated Continuous-Variable Quantum Key Distribution System via Power Analysis." Entropy 23, no. 2 (2021): 176. http://dx.doi.org/10.3390/e23020176.

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In quantum key distribution (QKD), there are some security loopholes opened by the gaps between the theoretical model and the practical system, and they may be exploited by eavesdroppers (Eve) to obtain secret key information without being detected. This is an effective quantum hacking strategy that seriously threatens the security of practical QKD systems. In this paper, we propose a new quantum hacking attack on an integrated silicon photonic continuous-variable quantum key distribution (CVQKD) system, which is known as a power analysis attack. This attack can be implemented by analyzing the

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Sweke, Ryan, Frederik Wilde, Johannes Jakob Meyer, et al. "Stochastic gradient descent for hybrid quantum-classical optimization." Quantum 4 (August 31, 2020): 314. http://dx.doi.org/10.22331/q-2020-08-31-314.

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Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of parameterized quantum circuits. In this work, we explore the consequences of the prior observation that estimation of these quantities on quantum hardware results in a form of stochastic gradient descent optimization. We formalize this notion, which allows us to show that in many relevant cases, including VQE, QAOA and certain quantum classifiers, estimating expectation values with k measurement outcomes results in

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Kumar, Manish. "Quantum Computing and Post Quantum Cryptography." International Journal of Innovative Research in Physics 2, no. 4 (2021): 37–51. http://dx.doi.org/10.15864/ijiip.2405.

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The present knowledge we had in quantum computer and the most possible architecture of a quantum computer might be able to break RSA 2048 in future. In classical computer two bits represents any one of four bit information whereas in quantum due to superposition it can be represent all four states. For ‘n’ qubits system is analogous to 2n classical bits. Quantum teleportation, quantum entanglement and other makes it possible to break present cryptosystem. Shor’s Algorithm is used for integer factorization which is polynomial time for quantum computer. This can be threat for RSA security. In th

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Perrier, Elija, Dacheng Tao, and Chris Ferrie. "Quantum geometric machine learning for quantum circuits and control." New Journal of Physics 22, no. 10 (2020): 103056. http://dx.doi.org/10.1088/1367-2630/abbf6b.

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Olivares-Sánchez, Julio, Jorge Casanova, Enrique Solano, and Lucas Lamata. "Measurement-Based Adaptation Protocol with Quantum Reinforcement Learning in a Rigetti Quantum Computer." Quantum Reports 2, no. 2 (2020): 293–304. http://dx.doi.org/10.3390/quantum2020019.

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We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer. The experiment in this few-qubit superconducting chip faithfully reproduces the theoretical proposal, setting the first steps towards a semiautonomous quantum agent. This experiment paves the way towards quantum reinforcement learning with superconducting circuits.

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Benedetti, Marcello, Erika Lloyd, Stefan Sack, and Mattia Fiorentini. "Parameterized quantum circuits as machine learning models." Quantum Science and Technology 4, no. 4 (2019): 043001. http://dx.doi.org/10.1088/2058-9565/ab4eb5.

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Chen, Samuel Yen-Chi, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, and Hsi-Sheng Goan. "Variational Quantum Circuits for Deep Reinforcement Learning." IEEE Access 8 (2020): 141007–24. http://dx.doi.org/10.1109/access.2020.3010470.

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Arunachalam, Srinivasan, Alex Bredariol Grilo, and Aarthi Sundaram. "Quantum Hardness of Learning Shallow Classical Circuits." SIAM Journal on Computing 50, no. 3 (2021): 972–1013. http://dx.doi.org/10.1137/20m1344202.

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31

Moll, Maximilian, and Leonhard Kunczik. "Comparing quantum hybrid reinforcement learning to classical methods." Human-Intelligent Systems Integration 3, no. 1 (2021): 15–23. http://dx.doi.org/10.1007/s42454-021-00025-3.

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AbstractIn recent history, reinforcement learning (RL) proved its capability by solving complex decision problems by mastering several games. Increased computational power and the advances in approximation with neural networks (NN) paved the path to RL’s successful applications. Even though RL can tackle more complex problems nowadays, it still relies on computational power and runtime. Quantum computing promises to solve these issues by its capability to encode information and the potential quadratic speedup in runtime. We compare tabular Q-learning and Q-learning using either a quantum or a

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Miatto, Filippo M., and Nicolás Quesada. "Fast optimization of parametrized quantum optical circuits." Quantum 4 (November 30, 2020): 366. http://dx.doi.org/10.22331/q-2020-11-30-366.

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Parametrized quantum optical circuits are a class of quantum circuits in which the carriers of quantum information are photons and the gates are optical transformations. Classically optimizing these circuits is challenging due to the infinite dimensionality of the photon number vector space that is associated to each optical mode. Truncating the space dimension is unavoidable, and it can lead to incorrect results if the gates populate photon number states beyond the cutoff. To tackle this issue, we present an algorithm that is orders of magnitude faster than the current state of the art, to re

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Bhatia, Amandeep Singh, Mandeep Kaur Saggi, Ajay Kumar, and Sushma Jain. "Matrix Product State–Based Quantum Classifier." Neural Computation 31, no. 7 (2019): 1499–517. http://dx.doi.org/10.1162/neco_a_01202.

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Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performa

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Meyer, Johannes Jakob. "Fisher Information in Noisy Intermediate-Scale Quantum Applications." Quantum 5 (September 9, 2021): 539. http://dx.doi.org/10.22331/q-2021-09-09-539.

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The recent advent of noisy intermediate-scale quantum devices, especially near-term quantum computers, has sparked extensive research efforts concerned with their possible applications. At the forefront of the considered approaches are variational methods that use parametrized quantum circuits. The classical and quantum Fisher information are firmly rooted in the field of quantum sensing and have proven to be versatile tools to study such parametrized quantum systems. Their utility in the study of other applications of noisy intermediate-scale quantum devices, however, has only been discovered

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Costa, Bruno, Pedro Branco, Manuel Goulão, Mariano Lemus, and Paulo Mateus. "Randomized Oblivious Transfer for Secure Multiparty Computation in the Quantum Setting." Entropy 23, no. 8 (2021): 1001. http://dx.doi.org/10.3390/e23081001.

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Secure computation is a powerful cryptographic tool that encompasses the evaluation of any multivariate function with arbitrary inputs from mutually distrusting parties. The oblivious transfer primitive serves is a basic building block for the general task of secure multi-party computation. Therefore, analyzing the security in the universal composability framework becomes mandatory when dealing with multi-party computation protocols composed of oblivious transfer subroutines. Furthermore, since the required number of oblivious transfer instances scales with the size of the circuits, oblivious

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Benedetti, Marcello, Erika Lloyd, Stefan Sack, and Mattia Fiorentini. "Erratum: Parameterized quantum circuits as machine learning models (2019 Quant. Sci. Tech. 4 043001)." Quantum Science and Technology 5, no. 1 (2019): 019601. http://dx.doi.org/10.1088/2058-9565/ab5944.

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Wocjan, P., and J. Yard. "The Jones polynomial: quantum algorithms and applications in quantum complexity theory." Quantum Information and Computation 8, no. 1&2 (2008): 147–80. http://dx.doi.org/10.26421/qic8.1-2-10.

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We analyze relationships between quantum computation and a family of generalizations of the Jones polynomial. Extending recent work by Aharonov et al., we give efficient quantum circuits for implementing the unitary Jones-Wenzl representations of the braid group. We use these to provide new quantum algorithms for approximately evaluating a family of specializations of the HOMFLYPT two-variable polynomial of trace closures of braids. We also give algorithms for approximating the Jones polynomial of a general class of closures of braids at roots of unity. Next we provide a self-contained proof o

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Pomarico, Domenico, Annarita Fanizzi, Nicola Amoroso, et al. "A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case." Mathematics 9, no. 4 (2021): 410. http://dx.doi.org/10.3390/math9040410.

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Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier a

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Asselmeyer-Maluga, Torsten. "Quantum computing and the brain: quantum nets, dessins d’enfants and neural networks." EPJ Web of Conferences 198 (2019): 00014. http://dx.doi.org/10.1051/epjconf/201919800014.

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In this paper, we will discuss a formal link between neural networks and quantum computing. For that purpose we will present a simple model for the description of the neural network by forming sub-graphs of the whole network with the same or a similar state. We will describe the interaction between these areas by closed loops, the feedback loops. The change of the graph is given by the deformations of the loops. This fact can be mathematically formalized by the fundamental group of the graph. Furthermore the neuron has two basic states |0〉 (ground state) and |1〉 (excited state). The whole stat

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40

Soref, Richard. "Reconfigurable Integrated Optoelectronics." Advances in OptoElectronics 2011 (May 4, 2011): 1–15. http://dx.doi.org/10.1155/2011/627802.

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Integrated optics today is based upon chips of Si and InP. The future of this chip industry is probably contained in the thrust towards optoelectronic integrated circuits (OEICs) and photonic integrated circuits (PICs) manufactured in a high-volume foundry. We believe that reconfigurable OEICs and PICs, known as ROEICs and RPICs, constitute the ultimate embodiment of integrated photonics. This paper shows that any ROEIC-on-a-chip can be decomposed into photonic modules, some of them fixed and some of them changeable in function. Reconfiguration is provided by electrical control signals to the

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Rieger, Carla, Cenk Tüysüz, Kristiane Novotny, et al. "Embedding of particle tracking data using hybrid quantum-classical neural networks." EPJ Web of Conferences 251 (2021): 03065. http://dx.doi.org/10.1051/epjconf/202125103065.

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The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatori

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Volobuev, A., and P. Romanchuk. "Genetics and Epigenetics of Sleep and Dreams." Bulletin of Science and Practice 6, no. 7 (2020): 176–217. http://dx.doi.org/10.33619/2414-2948/56/21.

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Multifunctional dream is an epigenetic gift to a person with great intelligence, new quantum ideas (each material object has quantum states and parallel worlds) and future inventions (discoveries). The circadian system of Homo sapiens and the structural-functional clock of the human body, are synchronized genetically and epigenetically. Life activity of H. sapiens is wave-shaped cyclic oscillations of various intensive processes of circadian stress. The multi-oscillator system, includes evolutionary structural-functional central and peripheral rhythm drivers, primary and secondary pacemakers.

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43

Mitarai, K., M. Negoro, M. Kitagawa, and K. Fujii. "Quantum circuit learning." Physical Review A 98, no. 3 (2018). http://dx.doi.org/10.1103/physreva.98.032309.

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Skolik, Andrea, Jarrod R. McClean, Masoud Mohseni, Patrick van der Smagt, and Martin Leib. "Layerwise learning for quantum neural networks." Quantum Machine Intelligence 3, no. 1 (2021). http://dx.doi.org/10.1007/s42484-020-00036-4.

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AbstractWith the increased focus on quantum circuit learning for near-term applications on quantum devices, in conjunction with unique challenges presented by cost function landscapes of parametrized quantum circuits, strategies for effective training are becoming increasingly important. In order to ameliorate some of these challenges, we investigate a layerwise learning strategy for parametrized quantum circuits. The circuit depth is incrementally grown during optimization, and only subsets of parameters are updated in each training step. We show that when considering sampling noise, this str

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Hubregtsen, Thomas, Josef Pichlmeier, Patrick Stecher, and Koen Bertels. "Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability." Quantum Machine Intelligence 3, no. 1 (2021). http://dx.doi.org/10.1007/s42484-021-00038-w.

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AbstractAn active area of investigation in the search for quantum advantage is quantum machine learning. Quantum machine learning, and parameterized quantum circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space. But is the ability of a quantum circuit to uniformly address the Hilbert space a good indicator of classification accuracy? In our work, we use methods and quantifications from prior art to perform a numerical study in order to evaluate the level of correlation. We find

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Chen, H., L. Wossnig, S. Severini, H. Neven, and M. Mohseni. "Universal discriminative quantum neural networks." Quantum Machine Intelligence 3, no. 1 (2020). http://dx.doi.org/10.1007/s42484-020-00025-7.

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AbstractRecent results have demonstrated the successful applications of quantum-classical hybrid methods to train quantum circuits for a variety of machine learning tasks. A natural question to ask is consequentially whether we can also train such quantum circuits to discriminate quantum data, i.e., perform classification on data stored in form of quantum states. Although quantum mechanics fundamentally forbids deterministic discrimination of non-orthogonal states, we show in this work that it is possible to train a quantum circuit to discriminate such data with a trade-off between minimizing

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Liu, Jin-Guo, and Lei Wang. "Differentiable learning of quantum circuit Born machines." Physical Review A 98, no. 6 (2018). http://dx.doi.org/10.1103/physreva.98.062324.

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Shi, Jinjing, Yongze Tang, Yuhu Lu, Yanyan Feng, Ronghua Shi, and Shichao Zhang. "Quantum Circuit Learning with Parameterized Boson Sampling." IEEE Transactions on Knowledge and Data Engineering, 2021, 1. http://dx.doi.org/10.1109/tkde.2021.3095103.

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M Subhi, Gharib, and Azeddine Messikh. "Simple quantum circuit for pattern recognition based on nearest mean classifier." International Journal on Perceptive and Cognitive Computing 2, no. 2 (2016). http://dx.doi.org/10.31436/ijpcc.v2i2.38.

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Machine learning plays a key role in many applications such as data mining and image recognition.Classification is one subcategory under machine learning. In this paper we propose a simple quantum circuitbased on the nearest mean classifier to classified handwriting characters. Our circuit is a simplified circuit fromthe quantum support vector machine [Phys. Rev. Lett. 114, 140504 (2015)] which uses quantum matrix inversealgorithm to find optimal hyperplane that separated two different classes. In our case the hyperplane is foundusing projections and rotations on the Bloch sphere.

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Maronese, Marco, and Enrico Prati. "A continuous rosenblatt quantum perceptron." International Journal of Quantum Information, June 30, 2021, 2140002. http://dx.doi.org/10.1142/s0219749921400025.

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Quantum neural networks are expected to provide the theoretical framework to implement machine learning on quantum computers. We develop a continuous Rosenblatt quantum perceptron which represents the generalization of the McCulloch–Pitts quantum perceptron existing in the literature. We implement its quantum circuit on a IBM quantum computer.

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